Talk given by Jonathan Pillow of the University of Texas at Austin. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley on May 20,2009.
Abstract. One of the central problems in theoretical neuroscience is to understand how ensembles of neurons convey information in their collective spiking activity. Correlations, or statistical dependencies between neural responses, can affect both the amount of information carried by population responses and the manner in which downstream brain areas can decode it. In this talk, I will present a model-based approach to understanding the neural code in populations of spiking neurons, using data from primate retina. A multivariate point-process model, formulated as a generalized linear model (GLM), provides an accurate and highly tractable description of the stimulus-dependence and the spatio-temporal correlation structure of the responses from a complete population of retinal ganglion cells. Bayesian decoding under this model provides a tool for assessing how correlations affect the information content of the neural code. I will discuss the implications of this framework for understanding the role of correlated activity in the encoding and decoding of sensory signals.